System and method for performing speech recognition in cyclostationary noise environments

ABSTRACT

A system and method for performing speech recognition in cyclostationary noise environments includes a characterization module that may access original cyclostationary noise from an intended operating environment of a speech recognition device. The characterization module may then convert the original cyclostationary noise into target stationary noise which retains characteristics of the original cyclostationary noise. A conversion module may then generate a modified training database by utilizing the target stationary noise to modify an original training database that was prepared for training a recognizer in the speech recognition device. A training module may then train the recognizer with the modified training database to thereby optimize speech recognition procedures in cyclostationary noise environments.

BACKGROUND SECTION

[0001] 1. Field of the Invention

[0002] This invention relates generally to electronic speech recognitionsystems, and relates more particularly to a method for performing speechrecognition in cyclostationary noise environments.

[0003] 2. Description of the Background Art

[0004] Implementing an effective and efficient method for system usersto interface with electronic devices is a significant consideration ofsystem designers and manufacturers. Automatic speech recognition is onepromising technique that allows a system user to effectively communicatewith selected electronic devices, such as digital computer systems.Speech typically consists of one or more spoken utterances which mayeach include a single word or a series of closely-spaced words forming aphrase or a sentence.

[0005] An automatic speech recognizer typically builds a comparisondatabase for performing speech recognition when a potential user“trains” the recognizer by providing a set of sample speech. Speechrecognizers tend to significantly degrade in performance when a mismatchexists between training conditions and actual operating conditions. Sucha mismatch may result from various types of acoustic distortion.

[0006] Conditions with significant ambient background-noise levelspresent additional difficulties when implementing a speech recognitionsystem. Examples of such noisy conditions may include speech recognitionin automobiles or in certain other mechanical devices. In such userapplications, in order to accurately analyze a particular utterance, aspeech recognition system may be required to selectively differentiatebetween a spoken utterance and the ambient background noise.

[0007] Referring now to FIG. 1(a), an exemplary waveform diagram for oneembodiment of clean speech 112 is shown. In addition, FIG. 1(b) depictsan exemplary waveform diagram for one embodiment of noisy speech 114 ina particular operating environment. In FIGS. 1(a) and 1(b), waveforms112 and 114 are presented for purposes of illustration only. A speechrecognition process may readily incorporate various other embodiments ofspeech waveforms.

[0008] From the foregoing discussion, it therefore becomes apparent thatcompensating for various types of ambient noise remains a significantconsideration of designers and manufacturers of contemporary speechrecognition systems.

SUMMARY

[0009] In accordance with the present invention, a method is disclosedfor performing speech recognition in cyclostationary noise environments.In one embodiment of the present invention, initially, originalcyclostationary noise from an intended operating environment of a speechrecognition device may preferably be provided to a characterizationmodule that may then preferably perform a cyclostationary noisecharacterization process to generate target stationary noise, inaccordance with the present invention.

[0010] In certain embodiments, the original cyclostationary noise maypreferably provided to a Fast Fourier Transform (FFT) from thecharacterization module. The FFT may then preferably generatefrequency-domain data by converting the original cyclostationary noisefrom the time domain to the frequency domain to produce acyclostationary noise frequency-power distribution. The cyclostationarynoise frequency-power distribution may include an array file withgroupings of power values that each correspond to a different frequency,wherein the groupings each correspond to a different time frame.

[0011] An averaging filter from the characterization module may thenaccess the cyclostationary noise frequency-power distribution, andresponsively generate an average cyclostationary noise frequency-powerdistribution using any effective techniques or methodologies. Forexample, the averaging filter may calculate an average cyclostationarypower value for each frequency of the cyclostationary noisefrequency-power distribution across the different time frames to therebyproduce the average cyclostationary noise frequency-power distributionwhich includes stationary characteristics of the originalcyclostationary noise.

[0012] Next, white noise with a flat power distribution across afrequency range may preferably be provided to the Fast Fourier Transform(FFT) of the characterization module. The FFT may then preferablygenerate frequency-domain data by converting the white noise from thetime domain to the frequency domain to produce a white noisefrequency-power distribution that may preferably include a series ofwhite noise power values that each correspond to a different frequency.

[0013] A modulation module of the characterization module may preferablyaccess the white noise frequency-power distribution, and may also accessthe foregoing average cyclostationary noise frequency-powerdistribution. The modulation module may then modulate white noise powervalues of the white noise frequency-power distribution withcorresponding cyclostationary power values from the averagecyclostationary noise frequency-power distribution to advantageouslygenerate a target stationary noise frequency-power distribution.

[0014] In certain embodiments, the modulation module may preferablygenerate individual target stationary power values of the targetstationary noise frequency-power distribution by multiplying individualwhite noise power values of the white noise frequency-power distributionwith corresponding individual cyclostationary power values from theaverage cyclostationary noise frequency-power distribution on afrequency-by-frequency basis. An Inverse Fast Fourier Transform (IFFT)of the characterization module may then preferably generate targetstationary noise by converting the target stationary noisefrequency-power distribution from the frequency domain to the timedomain.

[0015] A conversion module may preferably access an original trainingdatabase that was recorded for training a recognizer of the speechrecognition device based upon an intended speech recognition vocabularyof the speech recognition device. The conversion module may thenpreferably generate a modified training database by utilizing the targetstationary noise to modify the original training database. In practice,the conversion module may add the target stationary noise to theoriginal training database to produce the modified training databasethat then advantageously incorporates characteristics of the originalcyclostationary noise to improve performance of the speech recognitiondevice.

[0016] A training module may then access the modified training databasefor training the recognizer. Following the foregoing training process,the speech recognition device may then effectively utilize the trainedrecognizer to optimally perform various speech recognition functions.The present invention thus efficiently and effectively performs speechrecognition in cyclostationary noise environments.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017]FIG. 1(a) is an exemplary waveform diagram for one embodiment ofclean speech;

[0018]FIG. 1(b) is an exemplary waveform diagram for one embodiment ofnoisy speech;

[0019]FIG. 2 is a block diagram of one embodiment for a computer system,in accordance with the present invention;

[0020]FIG. 3 is a block diagram of one embodiment for the memory of FIG.2, in accordance with the present invention;

[0021]FIG. 4 is a block diagram of one embodiment for the speech moduleof FIG. 3, in accordance with the present invention;

[0022]FIG. 5 is a block diagram illustrating a cyclostationary noiseequalization procedure, in accordance with one embodiment of the presentinvention;

[0023]FIG. 6 is a diagram illustrating a cyclostationary noisecharacterization process, in accordance with one embodiment of thepresent invention;

[0024]FIG. 7 is a diagram illustrating a target noise generationprocess, in accordance with one embodiment of the present invention; and

[0025]FIG. 8 is a flowchart of method steps for performing acyclostationary noise equalization procedure, in accordance with oneembodiment of the present invention.

DETAILED DESCRIPTION

[0026] The present invention relates to an improvement in speechrecognition systems. The following description is presented to enableone of ordinary skill in the art to make and use the invention, and isprovided in the context of a patent application and its requirements.Various modifications to the preferred embodiment will be readilyapparent to those skilled in the art and the generic principles hereinmay be applied to other embodiments. Thus, the present invention is notintended to be limited to the embodiment shown, but is to be accordedthe widest scope consistent with the principles and features describedherein.

[0027] The present invention comprises a system and method forperforming speech recognition in cyclostationary noise environments, andmay preferably include a characterization module that may preferablyaccess original cyclostationary noise from an intended operatingenvironment of a speech recognition device. The characterization modulemay then preferably convert the original cyclostationary noise intotarget stationary noise which retains characteristics of the originalcyclostationary noise. A conversion module may then preferably generatea modified training database by utilizing the target stationary noise tomodify an original training database that was prepared for training arecognizer in the speech recognition device. A training module may thenadvantageously train the recognizer with the modified training databaseto thereby optimize speech recognition procedures in cyclostationarynoise environments.

[0028] Referring now to FIG. 2, a block diagram of one embodiment for acomputer system 210 is shown, in accordance with the present invention.The FIG. 2 embodiment includes a sound sensor 212, an amplifier 216, ananalog-to-digital converter 220, a central processing unit (CPU) 228, amemory 230 and an input/output device 232.

[0029] In operation, sound sensor 212 may be implemented as a microphonethat detects ambient sound energy and converts the detected sound energyinto an analog speech signal which is provided to amplifier 216 via line214. Amplifier 216 amplifies the received analog speech signal andprovides an amplified analog speech signal to analog-to-digitalconverter 220 via line 218. Analog-to-digital converter 220 thenconverts the amplified analog speech signal into corresponding digitalspeech data and provides the digital speech data via line 222 to systembus 224.

[0030] CPU 228 may then access the digital speech data on system bus 224and responsively analyze and process the digital speech data to performspeech recognition according to software instructions contained inmemory 230. The operation of CPU 228 and the software instructions inmemory 230 are further discussed below in conjunction with FIGS. 3-8.After the speech data is processed, CPU 228 may then advantageouslyprovide the results of the speech recognition analysis to other devices(not shown) via input/output interface 232.

[0031] Referring now to FIG. 3, a block diagram of one embodiment formemory 230 of FIG. 2 is shown. Memory 230 may alternatively comprisevarious storage-device configurations, including Random-Access Memory(RAM) and non-volatile storage devices such as floppy-disks or harddisk-drives. In the FIG. 3 embodiment, memory 230 may preferablyinclude, but is not limited to, a speech module 310, value registers312, cyclostationary noise 314, white noise 316, a characterizationmodule 316, a conversion module 318, an original training database, amodified training database, and a training module.

[0032] In the preferred embodiment, speech module 310 includes a seriesof software modules which are executed by CPU 228 to analyze andrecognizes speech data, and which are further described below inconjunction with FIG. 4. In alternate embodiments, speech module 310 mayreadily be implemented using various other software and/or hardwareconfigurations. Value registers 312, cyclostationary noise 314, whitenoise 315, characterization module 316, conversion module 318, originaltraining database 320, modified training database 322, and trainingmodule 324 are preferably utilized to effectively perform speechrecognition in cyclostationary noise environments, in accordance withthe present invention. The utilization and functionality of valueregisters 312, cyclostationary noise 314, white noise 315,characterization module 316, conversion module 318, original trainingdatabase 320, modified training database 322, and training module 324are further described below in conjunction with FIG. 5 through FIG. 8.

[0033] Referring now to FIG. 4, a block diagram for one embodiment ofthe FIG. 3 speech module 310 is shown. In the FIG. 3 embodiment, speechmodule 310 includes a feature extractor 410, an endpoint detector 414,and a recognizer 418.

[0034] In operation, analog-to-digital converter 220 (FIG. 2) providesdigital speech data to feature extractor 410 within speech module 310via system bus 224. Feature extractor 410 responsively generates featurevectors which are then provided to recognizer 418 via path 416. Endpointdetector 414 analyzes speech energy received from feature extractor 410,and responsively determines endpoints (beginning and ending points) forthe particular spoken utterance represented by the speech energyreceived via path 428. Endpoint detector 414 then provides thecalculated endpoints to recognizer 418 via path 432. Recognizer 418receives the feature vectors via path 416 and the endpoints via path432, and responsively performs a speech recognition procedure toadvantageously generate a speech recognition result to CPU 228 via path424. In the FIG. 4 embodiment, recognizer 418 may effectively beimplemented as a Hidden Markov Model (HMM) recognizer.

[0035] Referring now to FIG. 5, a block diagram illustrating acyclostationary noise equalization procedure is shown, in accordancewith one embodiment of the present invention. In alternate embodiments,the present invention may preferably perform a cyclostationary noiseequalization procedure using various other elements or functionalitiesin addition to, or instead of, those elements or functionalitiesdiscussed in conjunction with the FIG. 5 embodiment.

[0036] In addition, the FIG. 5 embodiment is discussed within thecontext of cyclostationary noise in an intended operating environment ofa speech recognition system. However, in alternate embodiments, theprinciples and techniques of the present invention may similarly beutilized to compensate for various other types of acoustic properties.For example, various techniques of the present invention may be utilizedto compensate for various other types of noise and other acousticartifacts.

[0037] In the FIG. 5 embodiment, initially, original cyclostationarynoise 314 from an intended operating environment of speech module 310 iscaptured and provided to characterization module 316 via path 512. Inthe FIG. 5 embodiment and elsewhere in this document, originalcyclostationary noise 314 may preferably include relatively stationaryambient noise that has a repeated cyclical pattern. For example, if thepower values of cyclostationary noise are plotted on a vertical axis ofa graph, and the frequency values of the cyclostationary noise areplotted on a horizontal axis of the same graph, then the shape of theresulting envelope may preferably remain approximately the unchangedover different time frames. The overall shape of the resulting envelopetypically depends upon the particular cyclostationary noise. However,the overall amplitude of the resulting envelope will vary oversuccessive time frames, depending upon the cyclic characteristics of thecyclostationary noise.

[0038] In the FIG. 5 embodiment, characterization module 316 may thenpreferably perform a cyclostationary noise characterization process togenerate target stationary noise 522 via path 516. One technique forperforming the foregoing cyclostationary noise characterization processis further discussed below in conjunction with FIGS. 6 and 7. Targetstationary noise 522 may then be provided to conversion module 318 viapath 524.

[0039] In the FIG. 5 embodiment, conversion module 318 may preferablyreceive an original training database 320 via path 526. The originaltraining database was preferably recorded for training recognizer 418 ofspeech module 310 based upon an intended speech recognition vocabularyof speech module 310.

[0040] In the FIG. 5 embodiment, conversion module 318 may thenpreferably generate a modified training database 322 via path 528 byutilizing target stationary noise 522 from path 524 to modify originaltraining database 320. In practice, conversion module 318 may add targetstationary noise 522 to original training database 320 to producemodified training database 322 that then advantageously incorporates thecharacteristics of original cyclostationary noise 314 to thereby improvethe performance of speech module 310.

[0041] In the FIG. 5 embodiment, training module 324 may then accessmodified training database 322 via path 529 to effectively trainrecognizer 418 via path 530. Techniques for training a speech recognizerare further discussed in “Fundamentals Of Speech Recognition,” byLawrence Rabiner and Biing-Hwang Juang, 1993, Prentice-Hall, Inc., whichis hereby incorporated by reference. Following the foregoing trainingprocess, speech module 310 may then effectively utilize the trainedrecognizer 418 as discussed above in conjunction with FIGS. 4 and 5 tooptimally perform various speech recognition functions.

[0042] Referring now to FIG. 6, a diagram illustrating a cyclostationarynoise characterization process is shown, in accordance with oneembodiment of the present invention. The foregoing cyclostationary noisecharacterization process may preferably be performed by characterizationmodule 316 as an initial part of a cyclostationary noisecharacterization procedure, as discussed above in conjunction with FIG.5, and as discussed below in conjunction with step 814 of FIG. 8. Inalternate embodiments, the present invention may perform acyclostationary noise characterization process by utilizing variousother elements or functionalities in addition to, or instead of, thoseelements or functionalities discussed in conjunction with the FIG. 6embodiment.

[0043] In addition, the FIG. 6 embodiment is discussed within thecontext cyclostationary noise of in an intended operating environment ofa speech recognition system. However, in alternate embodiments, theprinciples and techniques of the present invention may similarly beutilized to compensate for various other types of acoustic properties.For example, various techniques of the present invention may be utilizedto compensate for various other types of noise and other acousticartifacts.

[0044] In the FIG. 6 embodiment, initially, original cyclostationarynoise 314 from an intended operating environment of speech module 310may preferably be captured and provided to a Fast Fourier Transform(FFT) 614 of characterization module 316 via path 612. FFT 614 may thenpreferably generate frequency-domain data by converting the originalcyclostationary noise 314 from the time domain to the frequency domainto produce cyclostationary noise frequency-power distribution 618 viapath 616. Fast Fourier transforms are discussed in “Digital SignalProcessing Principles, Algorithms and Applications,” by John G. Proakisand Dimitris G. Manolakis, 1992, Macmillan Publishing Company, (inparticular, pages 706-708) which is hereby incorporated by reference.

[0045] In certain embodiments, cyclostationary noise frequency-powerdistribution 618 may include an array file with groupings of powervalues that each correspond to a different frequency, and wherein thegroupings each correspond to a different time frame. In other words,cyclostationary noise frequency-power distribution 618 may preferablyinclude an individual cyclostationary power value for each frequencyacross multiple time frames.

[0046] In the FIG. 6 embodiment, an averaging filter 626 may then accesscyclostationary noise frequency-power distribution 618 via path 624, andresponsively generate an average cyclostationary noise frequency-powerdistribution 630 on path 628 using any effective techniques ormethodologies. In the FIG. 6 embodiment, averaging filter 626 maypreferably calculate an average power value for each frequency ofcyclostationary noise frequency-power distribution 618 across thedifferent time frames to thereby produce average cyclostationary noisefrequency-power distribution 630 which then includes the stationarycharacteristics of original cyclostationary noise 314.

[0047] In the FIG. 5 embodiment, averaging filter 626 may preferablyperform an averaging operation according to the following formula:${{Average}\quad {CS}\quad {Power}_{k}} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{{CS}\quad {{Power}_{k}(t)}}}}$

[0048] where “k” represents frequency, “t” represents time frame, “N”represents total number of time frames, CS Power is a cyclostationarynoise power value from cyclostationary noise frequency-powerdistribution 618, and Average CS Power is an average cyclostationarynoise power value from average cyclostationary noise frequency-powerdistribution 630.

[0049] In the FIG. 6 embodiment, a modulation module 726 (FIGS. 3 and 7)may then access average cyclostationary noise frequency-powerdistribution 630 via path 632 and letter “A”, as further discussed belowin conjunction with FIG. 7.

[0050] Referring now to FIG. 7, a diagram illustrating a target noisegeneration process is shown, in accordance with one embodiment of thepresent invention. The foregoing target noise generation process maypreferably be performed by characterization module 316 as a final partof a cyclostationary noise characterization procedure, as discussedabove in conjunction with FIG. 5, and as discussed below in conjunctionwith step 814 of FIG. 8. In alternate embodiments, the present inventionmay readily perform a target noise generation process by utilizingvarious other elements or functionalities in addition to, or instead of,those elements or functionalities discussed in conjunction with the FIG.7 embodiment.

[0051] In addition, the FIG. 7 embodiment is discussed within thecontext of cyclostationary noise in an intended operating environment ofa speech recognition system. However, in alternate embodiments, theprinciples and techniques of the present invention may similarly beutilized to compensate for various other types of acousticcharacteristics. For example, various techniques of the presentinvention may be utilized to compensate for various other types of noiseand other acoustic artifacts.

[0052] In the FIG. 7 embodiment, initially, white noise 315 with a flatpower distribution across a frequency range may preferably be providedto a Fast Fourier Transform (FFT) 614 of characterization module 316 viapath 714. FFT 614 may then preferably generate frequency-domain data byconverting the white noise 315 from the time domain to the frequencydomain to produce white noise frequency-power distribution 718 via path716. In the FIG. 7 embodiment, white noise frequency-power distribution718 may preferably include a series of white noise power values thateach correspond to a given frequency.

[0053] In the FIG. 7 embodiment, a modulation module 726 ofcharacterization module 316 may preferably access white noisefrequency-power distribution 718 via path 724, and may also accessaverage cyclostationary noise frequency-power distribution 630 vialetter “A” and path 632 from foregoing FIG. 6. Modulation module 726 maythen modulate white noise power values of white noise frequency-powerdistribution 718 with corresponding cyclostationary power values fromaverage cyclostationary noise frequency-power distribution 630 togenerate target stationary noise frequency-power distribution 730 viapath 728.

[0054] In certain embodiments, modulation module 726 may preferablygenerate individual target stationary power values of target stationarynoise frequency-power distribution 730 by multiplying individual whitenoise power values of white noise frequency-power distribution 718 withcorresponding individual cyclostationary power values from averagecyclostationary noise frequency-power distribution 630 on afrequency-by-frequency basis. In the FIG. 7 embodiment, modulationmodule 726 may preferably modulate white noise frequency-powerdistribution 718 with average cyclostationary noise frequency-powerdistribution 630 in accordance with the following formula.

Target SN Power(t)_(k)=White Noise Power(t)_(k)×Average CS Power_(k)

[0055] where “k” represents frequency, “t” represents time frame, WhiteNoise Power is a white noise power value from white noisefrequency-power distribution 718, Average CS Power is an averagecyclostationary noise power value from average cyclostationary noisefrequency-power distribution 630, and Target SN Power is a targetstationary noise power value from target stationary noisefrequency-power distribution 730.

[0056] In the FIG. 7 embodiment, an Inverse Fast Fourier Transform(IFFT) 732 may then access target stationary noise frequency-powerdistribution 730 via path 731, and may preferably generate targetstationary noise 736 on path 734 by converting target stationary noisefrequency-power distribution 730 from the frequency domain to the timedomain. Conversion module 318 (FIG. 5) may then access target stationarynoise 736 via path 524, as discussed above in conjunction with foregoingFIG. 5.

[0057] Referring now to FIG. 8, a flowchart of method steps forperforming a cyclostationary noise equalization procedure is shown, inaccordance with one embodiment of the present invention. The FIG. 8embodiment is presented for purposes of illustration, and in alternateembodiments, the present invention may readily utilize various steps andsequences other than those discussed in conjunction with the FIG. 8embodiment.

[0058] In addition, the FIG. 8 embodiment is discussed within thecontext cyclostationary noise in an intended operating environment of aspeech recognition system. However, in alternate embodiments, theprinciples and techniques of the present invention may be similarlyutilized to compensate for various other types of acoustic properties.For example, various techniques of the present invention may be utilizedto compensate for various other types of noise and other acousticartifacts.

[0059] In the FIG. 8 embodiment, in step 812, original cyclostationarynoise 314 from an intended operating environment of speech module 310may preferably be captured and provided to a characterization module316. In step 814, characterization module 316 may then preferablyperform a cyclostationary noise characterization process to generatetarget stationary noise 522, as discussed above in conjunction withFIGS. 6 and 7.

[0060] In step 816 of the FIG. 8 embodiment, a conversion module 318 maypreferably access an original training database 320, and may thenresponsively generate a modified training database 322 by utilizingtarget stationary noise 522 to modify the original training database320. In certain embodiments, conversion module 318 may add targetstationary noise 522 to original training database 320 to producemodified training database 322 that then advantageously incorporatescharacteristics of original cyclostationary noise 314 to thereby improvespeech recognition operations.

[0061] In the FIG. 8 embodiment, a training module 324 may then accessmodified training database 322 to effectively train a recognizer 418 ina speech module 310. Following the foregoing training process, thespeech module 310 may then effectively utilize the trained recognizer418 as discussed above in conjunction with FIGS. 4 and 5 to optimallyperform various speech recognition functions.

[0062] The invention has been explained above with reference to apreferred embodiment. Other embodiments will be apparent to thoseskilled in the art in light of this disclosure. For example, the presentinvention may readily be implemented using configurations and techniquesother than those described in the preferred embodiment above.Additionally, the present invention may effectively be used inconjunction with systems other than the one described above as thepreferred embodiment. Therefore, these and other variations upon thepreferred embodiments are intended to be covered by the presentinvention, which is limited only by the appended claims.

What is claimed is:
 1. A system for performing a cyclostationary noiseequalization procedure in a speech recognition device, comprising: acharacterization module configured to convert original cyclostationarynoise data from an operating environment of said speech recognitiondevice into target stationary noise data by performing a cyclostationarynoise characterization process; and a conversion module coupled to saidcharacterization module for converting an original training databaseinto a modified training database by incorporating said targetstationary noise data into said original training database, saidmodified training database then being utilized to train a recognizerfrom said speech recognition device.
 2. The system of claim 1 whereinsaid speech recognition device is implemented as part of a roboticdevice to compensate for cyclostationary noise in said operatingenvironment of said robotic device.
 3. The system of claim 1 whereinsaid original cyclostationary noise data is recorded, digitized, andstored in a memory device for access by said characterization module. 4.The system of claim 1 wherein a Fast Fourier Transform of saidcharacterization module converts said original cyclostationary noisedata from a time domain to a frequency domain to produce acyclostationary noise frequency-power distribution.
 5. The system ofclaim 4 wherein said cyclostationary noise frequency-power distributionincludes an array file with groupings of power values that eachcorrespond to a different cyclostationary frequency, and wherein saidgroupings each correspond to a different time frame.
 6. The system ofclaim 4 wherein an averaging filter accesses said cyclostationary noisefrequency-power distribution, and responsively generates an averagecyclostationary noise frequency-power distribution.
 7. The system ofclaim 6 wherein said averaging filter calculates an averagecyclostationary power value for each frequency of said cyclostationarynoise frequency-power distribution across different time frames tothereby produce said average cyclostationary noise frequency-powerdistribution which characterizes stationary noise characteristics ofsaid original cyclostationary noise data.
 8. The system of claim 6wherein said averaging filter performs an averaging operation accordingto a following formula:${{Average}\quad {CS}\quad {Power}_{k}} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{{CS}\quad {{Power}_{k}(t)}}}}$

where said “k” represents a frequency, said “t” represents a time frame,said “N” represents a total number of time frames, said CS Power is acyclostationary noise power value from said cyclostationary noisefrequency-power distribution, and said Average CS Power is an averagecyclostationary power value from said average cyclostationary noisefrequency-power distribution.
 9. The system of claim 6 wherein saidcharacterization module accesses white noise data that has a uniformpower distribution across a given frequency range.
 10. The system ofclaim 9 wherein said Fast Fourier Transform of said characterizationmodule converts said white noise data from said time domain to saidfrequency domain to produce a white noise frequency-power distribution.11. The system of claim 10 wherein said white noise frequency-powerdistribution includes a series of white noise power values that eachcorrespond to a particular frequency.
 12. The system of claim 10 whereina modulation module of said characterization module utilizes said whitenoise frequency-power distribution and said average cyclostationarynoise frequency-power distribution to generate a target stationary noisefrequency-power distribution.
 13. The system of claim 12 wherein saidmodulation module modulates said white noise power values of said whitenoise frequency-power distribution with corresponding ones of saidcyclostationary power values from said average cyclostationary noisefrequency-power distribution to thereby generate said target stationarynoise frequency-power distribution.
 14. The system of claim 12 whereinsaid modulation module generates individual target stationary powervalues of said target stationary noise frequency-power distribution bymultiplying individual ones of said white noise power values from saidwhite noise frequency-power distribution with corresponding ones of saidcyclostationary power values from said average cyclostationary noisefrequency-power distribution on a frequency-by-frequency basis.
 15. Thesystem of claim 12 wherein said modulation module modulates said whitenoise frequency-power distribution with said average cyclostationarynoise frequency-power distribution in accordance with a followingformula: Target SN Power(t)_(k)=White Noise Power(t)_(k)×Average CSPower_(k) where said “k” represents a frequency, said “t” represents atime frame, said White Noise Power is a white noise power value fromsaid white noise frequency-power distribution, said Average CS Power isan average cyclostationary power value from said average cyclostationarynoise frequency-power distribution, and said Target SN Power is a targetstationary power value from said target stationary noise frequency-powerdistribution.
 16. The system of claim 12 wherein an Inverse Fast FourierTransform accesses said target stationary noise frequency-powerdistribution to generate target stationary noise data by converting saidtarget stationary noise frequency-power distribution from said frequencydomain to said time domain.
 17. The system of claim 16 wherein aconversion module accesses an original training database that wasrecorded for training said recognizer based upon an intended speechrecognition vocabulary of said speech recognition system, saidconversion module responsively generating a modified training databaseby utilizing said target stationary noise data to modify said originaltraining database.
 18. The system of claim 17 wherein said conversionmodule adds said target stationary noise data to said original trainingdatabase to produce said modified training database that thenincorporates characteristics of said original cyclostationary noise datato thereby improve performance characteristics of said speechrecognition device.
 19. The system of claim 17 wherein a training moduleaccesses said modified training database to perform a speech recognitiontraining procedure to train said recognizer.
 20. The system of claim 19wherein said speech recognition device utilizes said recognizer aftersaid speech recognition training procedure with said modified trainingdatabase has been completed to thereby optimally perform various speechrecognition functions.
 21. A method for performing a cyclostationarynoise equalization procedure in a speech recognition device, comprisingthe steps of: converting original cyclostationary noise data from anoperating environment of said speech recognition device into targetstationary noise data with a characterization module by performing acyclostationary noise characterization process; converting an originaltraining database into a modified training database with a conversionmodule by incorporating said target stationary noise data into saidoriginal training database; and training a recognizer from said speechrecognition device by utilizing said modified training database.
 22. Themethod of claim 21 wherein said speech recognition device is implementedas part of a robotic device to compensate for cyclostationary noise insaid operating environment of said robotic device.
 23. The method ofclaim 21 wherein said original cyclostationary noise data is recorded,digitized, and stored in a memory device for access by saidcharacterization module.
 24. The method of claim 21 wherein a FastFourier Transform of said characterization module converts said originalcyclostationary noise data from a time domain to a frequency domain toproduce a cyclostationary noise frequency-power distribution.
 25. Themethod of claim 24 wherein said cyclostationary noise frequency-powerdistribution includes an array file with groupings of power values thateach correspond to a different cyclostationary frequency, and whereinsaid groupings each correspond to a different time frame.
 26. The methodof claim 24 wherein an averaging filter accesses said cyclostationarynoise frequency-power distribution, and responsively generates anaverage cyclostationary noise frequency-power distribution.
 27. Themethod of claim 26 wherein said averaging filter calculates an averagecyclostationary power value for each frequency of said cyclostationarynoise frequency-power distribution across different time frames tothereby produce said average cyclostationary noise frequency-powerdistribution which characterizes stationary noise characteristics ofsaid original cyclostationary noise data.
 28. The method of claim 26wherein said averaging filter performs an averaging operation accordingto a following formula:${{Average}\quad {CS}\quad {Power}_{k}} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{{CS}\quad {{Power}_{k}(t)}}}}$

where said “k” represents a frequency, said “t” represents a time frame,said “N” represents a total number of time frames, said CS Power is acyclostationary noise power value from said cyclostationary noisefrequency-power distribution, and said Average CS Power is an averagecyclostationary power value from said average cyclostationary noisefrequency-power distribution.
 29. The method of claim 26 wherein saidcharacterization module accesses white noise data that has a uniformpower distribution across a given frequency range.
 30. The method ofclaim 29 wherein said Fast Fourier Transform of said characterizationmodule converts said white noise data from said time domain to saidfrequency domain to produce a white noise frequency-power distribution.31. The method of claim 30 wherein said white noise frequency-powerdistribution includes a series of white noise power values that eachcorrespond to a particular frequency.
 32. The method of claim 30 whereina modulation module of said characterization module utilizes said whitenoise frequency-power distribution and said average cyclostationarynoise frequency-power distribution to generate a target stationary noisefrequency-power distribution.
 33. The method of claim 32 wherein saidmodulation module modulates said white noise power values of said whitenoise frequency-power distribution with corresponding ones of saidcyclostationary power values from said average cyclostationary noisefrequency-power distribution to thereby generate said target stationarynoise frequency-power distribution.
 34. The method of claim 32 whereinsaid modulation module generates individual target stationary powervalues of said target stationary noise frequency-power distribution bymultiplying individual ones of said white noise power values from saidwhite noise frequency-power distribution with corresponding ones of saidcyclostationary power values from said average cyclostationary noisefrequency-power distribution on a frequency-by-frequency basis.
 35. Themethod of claim 32 wherein said modulation module modulates said whitenoise frequency-power distribution with said average cyclostationarynoise frequency-power distribution in accordance with a followingformula: Target SN Power(t)_(k)=White Noise Power(t)_(k)×Average CSPower_(k) where said “k” represents a frequency, said “t” represents atime frame, said White Noise Power is a white noise power value fromsaid white noise frequency-power distribution, said Average CS Power isan average cyclostationary power value from said average cyclostationarynoise frequency-power distribution, and said Target SN Power is a targetstationary power value from said target stationary noise frequency-powerdistribution.
 36. The method of claim 32 wherein an Inverse Fast FourierTransform accesses said target stationary noise frequency-powerdistribution to generate target stationary noise data by converting saidtarget stationary noise frequency-power distribution from said frequencydomain to said time domain.
 37. The method of claim 36 wherein aconversion module accesses an original training database that wasrecorded for training said recognizer based upon an intended speechrecognition vocabulary of said speech recognition system, saidconversion module responsively generating a modified training databaseby utilizing said target stationary noise data to modify said originaltraining database.
 38. The method of claim 37 wherein said conversionmodule adds said target stationary noise data to said original trainingdatabase to produce said modified training database that thenincorporates characteristics of said original cyclostationary noise datato thereby improve performance characteristics of said speechrecognition device.
 39. The method of claim 37 wherein a training moduleaccesses said modified training database to perform a speech recognitiontraining procedure to train said recognizer.
 40. The method of claim 39wherein said speech recognition device utilizes said recognizer aftersaid speech recognition training procedure with said modified trainingdatabase has been completed to thereby optimally perform various speechrecognition functions.
 41. An apparatus for performing a cyclostationarynoise equalization procedure in a speech recognition device, comprising:means for converting original cyclostationary noise data from anoperating environment of said speech recognition device into targetstationary noise data by performing a cyclostationary noisecharacterization process; means for converting an original trainingdatabase into a modified training database by incorporating said targetstationary noise data into said original training database; and meansfor training a recognizer from said speech recognition device byutilizing said modified training database.
 42. A computer-readablemedium comprising program instructions for performing a cyclostationarynoise equalization procedure in a speech recognition device byperforming the steps of: converting original cyclostationary noise datafrom an operating environment of said speech recognition device intotarget stationary noise data with a characterization module byperforming a cyclostationary noise characterization process; convertingan original training database into a modified training database with aconversion module by incorporating said target stationary noise datainto said original training database; and training a recognizer fromsaid speech recognition device by utilizing said modified trainingdatabase.
 43. A system for performing a noise equalization procedure ina speech recognition device, comprising: a characterization moduleconfigured to convert original noise data from an operating environmentof said speech recognition device into target noise data; and aconversion module coupled to said characterization module for convertingan original training database into a modified training database byincorporating said target noise data into said original trainingdatabase, said modified training database then being utilized to train arecognizer from said speech recognition device.